An impressive list of 24 ML engineering rules!
It’s tempting to get caught in the trap of applying a promising new technology like Machine Learning to every problem, but try and resist this temptation. Instead, try to follow Joshua Porter’s advice as he states in his Principles of Product Design:
“…[P]roduct innovation isn’t about new products that solve new problems. Product innovation is about new products that solve existing problems better than they’re currently solved.”
While reading Hung Lee’s Recruiting Brainfood, I stumbled upon this deep learning primer:
The primer is great, and a quick read. Here is my quick summary below:
The basics of deep learning is to think about how the brain breaks up a specific task. For example, let’s say you are hiking the Appalachian Trail, and you see something in the distance running towards you. First, you might notice it is moving. Then, you might notice what shape it is. Then, you might notice how fast it is going. Then, you might notice a big snout. Then, your brain will determine that this is an animal.
The process would continue until your brain evaluated, classified and predicts what object it is seeing. The joy of the mental exercise (for me) is to understand how the human mind works to break down ideas.
Inputs > Algorithm > Prediction > Training:
The following are the key concepts for thinking about deep learning concepts. Yes, this is overly simplified, but it is still a helpful start.
- Inputs: Labels/Images
- Levels of Abstraction 1: Is this a shape?
- Level of Abstraction 2: Is this shape an ear?
- Level of Abstraction 3: Is this a cat?
- Prediction = Yes or No. Is this prediction correct?
Current-State of Deep Learning:
- Supervised Deep Learning: In effect, this is attempting to clone human behavior via labeled images, video, text or speech.
- Reinforcement Learning: This is where the model attempts to “learn” behaviors, codify those behaviors (i.e. what does that mean), and then implement strategies to optimize based on those strategies. As the article suggests, the following are some examples:
- E-Commerce: model learns customer behaviors and tailors service to suit customer interests.
- Finance: model learns market behavior and generates trading strategies.
- Robots: model learns how physical world behaves (through video) and then navigates that world.
Network Architecture to Detect Objects in Images:
- Input: Image
- Extract Feature: Extract the specific features
- Classification: Classify based on the probability of those features
- Output: Image prediction
Enjoy your deep learning explorations!
AI and Machine Learning will slowly arrive make its way into all of our apps in both seen and unseen ways.
How do we embrace this? How do we plan for this? How will this change the work all of us do?
Here’s Microsoft’s “Design Ideas” in PowerPoint:
In the article, “New AI Can Diagnose Pneumonia Better Than Doctors (https://www.fastcodesign.com/90152230/new-ai-can-diagnose-pneumonia-better-than-doctors) we begin to see a glimpse of the possibilities:
“In the case of CheXnet, the research team led by Stanford adjunct professor Andrew Ng, started by training the neural network with 112,120 chest X-ray images that were previously manually labeled with up to 14 different diseases. One of them was pneumonia. After training it for a month, the software beat previous computer-based methods to detect this type of infection. The Stanford Machine Learning Group team pitted its software against four Stanford radiologists, giving each of them 420 X-ray images. This graphic shows how the radiologists–represented by the orange Xs–did compared to the program–represented by the blue curve.”